{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Transfer Learning for Computer Vision Tutorial\n",
    "==============================================\n",
    "**Author**: `Sasank Chilamkurthy <https://chsasank.github.io>`_\n",
    "\n",
    "In this tutorial, you will learn how to train a convolutional neural network for\n",
    "image classification using transfer learning. You can read more about the transfer\n",
    "learning at `cs231n notes <https://cs231n.github.io/transfer-learning/>`__\n",
    "\n",
    "Quoting these notes,\n",
    "\n",
    "    In practice, very few people train an entire Convolutional Network\n",
    "    from scratch (with random initialization), because it is relatively\n",
    "    rare to have a dataset of sufficient size. Instead, it is common to\n",
    "    pretrain a ConvNet on a very large dataset (e.g. ImageNet, which\n",
    "    contains 1.2 million images with 1000 categories), and then use the\n",
    "    ConvNet either as an initialization or a fixed feature extractor for\n",
    "    the task of interest.\n",
    "\n",
    "These two major transfer learning scenarios look as follows:\n",
    "\n",
    "-  **Finetuning the convnet**: Instead of random initializaion, we\n",
    "   initialize the network with a pretrained network, like the one that is\n",
    "   trained on imagenet 1000 dataset. Rest of the training looks as\n",
    "   usual.\n",
    "-  **ConvNet as fixed feature extractor**: Here, we will freeze the weights\n",
    "   for all of the network except that of the final fully connected\n",
    "   layer. This last fully connected layer is replaced with a new one\n",
    "   with random weights and only this layer is trained.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# License: BSD\n",
    "# Author: Sasank Chilamkurthy\n",
    "\n",
    "from __future__ import print_function, division\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.optim import lr_scheduler\n",
    "import numpy as np\n",
    "import torchvision\n",
    "from torchvision import datasets, models, transforms\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import os\n",
    "import copy\n",
    "\n",
    "plt.ion()   # interactive mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "torch.cuda.is_available()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load Data\n",
    "---------\n",
    "\n",
    "We will use torchvision and torch.utils.data packages for loading the\n",
    "data.\n",
    "\n",
    "The problem we're going to solve today is to train a model to classify\n",
    "**ants** and **bees**. We have about 120 training images each for ants and bees.\n",
    "There are 75 validation images for each class. Usually, this is a very\n",
    "small dataset to generalize upon, if trained from scratch. Since we\n",
    "are using transfer learning, we should be able to generalize reasonably\n",
    "well.\n",
    "\n",
    "This dataset is a very small subset of imagenet.\n",
    "\n",
    ".. Note ::\n",
    "   Download the data from\n",
    "   `here <https://download.pytorch.org/tutorial/hymenoptera_data.zip>`_\n",
    "   and extract it to the current directory.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data augmentation and normalization for training\n",
    "# Just normalization for validation\n",
    "data_transforms = {\n",
    "    'train': transforms.Compose([\n",
    "        transforms.RandomResizedCrop(224),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "    ]),\n",
    "    'val': transforms.Compose([\n",
    "        transforms.Resize(256),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "    ]),\n",
    "}\n",
    "\n",
    "data_dir = 'data/dat_mul'\n",
    "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),\n",
    "                                          data_transforms[x])\n",
    "                  for x in ['train', 'val']}\n",
    "dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,\n",
    "                                             shuffle=True, num_workers=4)\n",
    "              for x in ['train', 'val']}\n",
    "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}\n",
    "class_names = image_datasets['train'].classes\n",
    "\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "#device = torch.device(\"cpu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize a few images\n",
    "^^^^^^^^^^^^^^^^^^^^^^\n",
    "Let's visualize a few training images so as to understand the data\n",
    "augmentations.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def imshow(inp, title=None):\n",
    "    \"\"\"Imshow for Tensor.\"\"\"\n",
    "    inp = inp.numpy().transpose((1, 2, 0))\n",
    "    mean = np.array([0.485, 0.456, 0.406])\n",
    "    std = np.array([0.229, 0.224, 0.225])\n",
    "    inp = std * inp + mean\n",
    "    inp = np.clip(inp, 0, 1)\n",
    "    plt.imshow(inp)\n",
    "    if title is not None:\n",
    "        plt.title(title)\n",
    "    plt.pause(0.001)  # pause a bit so that plots are updated\n",
    "\n",
    "\n",
    "# Get a batch of training data\n",
    "inputs, classes = next(iter(dataloaders['train']))\n",
    "\n",
    "# Make a grid from batch\n",
    "out = torchvision.utils.make_grid(inputs)\n",
    "\n",
    "imshow(out, title=[class_names[x] for x in classes])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training the model\n",
    "------------------\n",
    "\n",
    "Now, let's write a general function to train a model. Here, we will\n",
    "illustrate:\n",
    "\n",
    "-  Scheduling the learning rate\n",
    "-  Saving the best model\n",
    "\n",
    "In the following, parameter ``scheduler`` is an LR scheduler object from\n",
    "``torch.optim.lr_scheduler``.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n",
    "    since = time.time()\n",
    "\n",
    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
    "    best_acc = 0.0\n",
    "\n",
    "    for epoch in range(num_epochs):\n",
    "        print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n",
    "        print('-' * 10)\n",
    "\n",
    "        # Each epoch has a training and validation phase\n",
    "        for phase in ['train', 'val']:\n",
    "            if phase == 'train':\n",
    "                model.train()  # Set model to training mode\n",
    "            else:\n",
    "                model.eval()   # Set model to evaluate mode\n",
    "\n",
    "            running_loss = 0.0\n",
    "            running_corrects = 0\n",
    "\n",
    "            # Iterate over data.\n",
    "            for inputs, labels in dataloaders[phase]:\n",
    "                inputs = inputs.to(device)\n",
    "                labels = labels.to(device)\n",
    "\n",
    "                # zero the parameter gradients\n",
    "                optimizer.zero_grad()\n",
    "\n",
    "                # forward\n",
    "                # track history if only in train\n",
    "                with torch.set_grad_enabled(phase == 'train'):\n",
    "                    outputs = model(inputs)\n",
    "                    _, preds = torch.max(outputs, 1)\n",
    "                    loss = criterion(outputs, labels)\n",
    "\n",
    "                    # backward + optimize only if in training phase\n",
    "                    if phase == 'train':\n",
    "                        loss.backward()\n",
    "                        optimizer.step()\n",
    "\n",
    "                # statistics\n",
    "                running_loss += loss.item() * inputs.size(0)\n",
    "                running_corrects += torch.sum(preds == labels.data)\n",
    "            if phase == 'train':\n",
    "                scheduler.step()\n",
    "\n",
    "            epoch_loss = running_loss / dataset_sizes[phase]\n",
    "            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
    "\n",
    "            print('{} Loss: {:.4f} Acc: {:.4f}'.format(\n",
    "                phase, epoch_loss, epoch_acc))\n",
    "\n",
    "            # deep copy the model\n",
    "            if phase == 'val' and epoch_acc > best_acc:\n",
    "                best_acc = epoch_acc\n",
    "                best_model_wts = copy.deepcopy(model.state_dict())\n",
    "\n",
    "        print()\n",
    "\n",
    "    time_elapsed = time.time() - since\n",
    "    print('Training complete in {:.0f}m {:.0f}s'.format(\n",
    "        time_elapsed // 60, time_elapsed % 60))\n",
    "    print('Best val Acc: {:4f}'.format(best_acc))\n",
    "\n",
    "    # load best model weights\n",
    "    model.load_state_dict(best_model_wts)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing the model predictions\n",
    "^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
    "\n",
    "Generic function to display predictions for a few images\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_model(model, num_images=6):\n",
    "    was_training = model.training\n",
    "    model.eval()\n",
    "    images_so_far = 0\n",
    "    fig = plt.figure()\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for i, (inputs, labels) in enumerate(dataloaders['val']):\n",
    "            inputs = inputs.to(device)\n",
    "            labels = labels.to(device)\n",
    "\n",
    "            outputs = model(inputs)\n",
    "            _, preds = torch.max(outputs, 1)\n",
    "\n",
    "            for j in range(inputs.size()[0]):\n",
    "                images_so_far += 1\n",
    "                ax = plt.subplot(num_images//2, 2, images_so_far)\n",
    "                ax.axis('off')\n",
    "                ax.set_title('predicted: {}'.format(class_names[preds[j]]))\n",
    "                imshow(inputs.cpu().data[j])\n",
    "\n",
    "                if images_so_far == num_images:\n",
    "                    model.train(mode=was_training)\n",
    "                    return\n",
    "        model.train(mode=was_training)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finetuning the convnet\n",
    "----------------------\n",
    "\n",
    "Load a pretrained model and reset final fully connected layer.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_ft = models.resnet18(pretrained=True)\n",
    "num_ftrs = model_ft.fc.in_features\n",
    "# Here the size of each output sample is set to 2.\n",
    "# Alternatively, it can be generalized to nn.Linear(num_ftrs, len(class_names)).\n",
    "model_ft.fc = nn.Linear(num_ftrs, 3)\n",
    "\n",
    "model_ft = model_ft.to(device)\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# Observe that all parameters are being optimized\n",
    "optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)\n",
    "\n",
    "# Decay LR by a factor of 0.1 every 7 epochs\n",
    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train and evaluate\n",
    "^^^^^^^^^^^^^^^^^^\n",
    "\n",
    "It should take around 15-25 min on CPU. On GPU though, it takes less than a\n",
    "minute.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/24\n",
      "----------\n",
      "train Loss: 0.5965 Acc: 0.7118\n",
      "val Loss: 0.1998 Acc: 0.9382\n",
      "\n",
      "Epoch 1/24\n",
      "----------\n",
      "train Loss: 0.3095 Acc: 0.8941\n",
      "val Loss: 0.1998 Acc: 0.9636\n",
      "\n",
      "Epoch 2/24\n",
      "----------\n",
      "train Loss: 0.2881 Acc: 0.8882\n",
      "val Loss: 0.1314 Acc: 0.9782\n",
      "\n",
      "Epoch 3/24\n",
      "----------\n",
      "train Loss: 0.2863 Acc: 0.9000\n",
      "val Loss: 0.1973 Acc: 0.9636\n",
      "\n",
      "Epoch 4/24\n",
      "----------\n",
      "train Loss: 0.3319 Acc: 0.8824\n",
      "val Loss: 0.1867 Acc: 0.9673\n",
      "\n",
      "Epoch 5/24\n",
      "----------\n",
      "train Loss: 0.3413 Acc: 0.8706\n",
      "val Loss: 0.1424 Acc: 0.9636\n",
      "\n",
      "Epoch 6/24\n",
      "----------\n",
      "train Loss: 0.3262 Acc: 0.8706\n",
      "val Loss: 0.2660 Acc: 0.9673\n",
      "\n",
      "Epoch 7/24\n",
      "----------\n",
      "train Loss: 0.1392 Acc: 0.9471\n",
      "val Loss: 0.2475 Acc: 0.9709\n",
      "\n",
      "Epoch 8/24\n",
      "----------\n",
      "train Loss: 0.1138 Acc: 0.9412\n",
      "val Loss: 0.1838 Acc: 0.9782\n",
      "\n",
      "Epoch 9/24\n",
      "----------\n",
      "train Loss: 0.0894 Acc: 0.9765\n",
      "val Loss: 0.1780 Acc: 0.9782\n",
      "\n",
      "Epoch 10/24\n",
      "----------\n",
      "train Loss: 0.1969 Acc: 0.9412\n",
      "val Loss: 0.2207 Acc: 0.9782\n",
      "\n",
      "Epoch 11/24\n",
      "----------\n",
      "train Loss: 0.1125 Acc: 0.9706\n",
      "val Loss: 0.1685 Acc: 0.9818\n",
      "\n",
      "Epoch 12/24\n",
      "----------\n",
      "train Loss: 0.1257 Acc: 0.9647\n",
      "val Loss: 0.1779 Acc: 0.9818\n",
      "\n",
      "Epoch 13/24\n",
      "----------\n",
      "train Loss: 0.0725 Acc: 0.9824\n",
      "val Loss: 0.1639 Acc: 0.9818\n",
      "\n",
      "Epoch 14/24\n",
      "----------\n",
      "train Loss: 0.0743 Acc: 0.9765\n",
      "val Loss: 0.1826 Acc: 0.9782\n",
      "\n",
      "Epoch 15/24\n",
      "----------\n",
      "train Loss: 0.1158 Acc: 0.9529\n",
      "val Loss: 0.1374 Acc: 0.9818\n",
      "\n",
      "Epoch 16/24\n",
      "----------\n",
      "train Loss: 0.0833 Acc: 0.9765\n",
      "val Loss: 0.1679 Acc: 0.9818\n",
      "\n",
      "Epoch 17/24\n",
      "----------\n",
      "train Loss: 0.0427 Acc: 0.9941\n",
      "val Loss: 0.1294 Acc: 0.9818\n",
      "\n",
      "Epoch 18/24\n",
      "----------\n",
      "train Loss: 0.1044 Acc: 0.9647\n",
      "val Loss: 0.1726 Acc: 0.9818\n",
      "\n",
      "Epoch 19/24\n",
      "----------\n",
      "train Loss: 0.0886 Acc: 0.9647\n",
      "val Loss: 0.1854 Acc: 0.9782\n",
      "\n",
      "Epoch 20/24\n",
      "----------\n",
      "train Loss: 0.1285 Acc: 0.9647\n",
      "val Loss: 0.1883 Acc: 0.9818\n",
      "\n",
      "Epoch 21/24\n",
      "----------\n",
      "train Loss: 0.1444 Acc: 0.9529\n",
      "val Loss: 0.2142 Acc: 0.9782\n",
      "\n",
      "Epoch 22/24\n",
      "----------\n",
      "train Loss: 0.0980 Acc: 0.9706\n",
      "val Loss: 0.1977 Acc: 0.9782\n",
      "\n",
      "Epoch 23/24\n",
      "----------\n",
      "train Loss: 0.1036 Acc: 0.9765\n",
      "val Loss: 0.1315 Acc: 0.9818\n",
      "\n",
      "Epoch 24/24\n",
      "----------\n",
      "train Loss: 0.1286 Acc: 0.9647\n",
      "val Loss: 0.1685 Acc: 0.9818\n",
      "\n",
      "Training complete in 3m 11s\n",
      "Best val Acc: 0.981818\n"
     ]
    }
   ],
   "source": [
    "model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,\n",
    "                       num_epochs=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bqdnkJI8DFguzB+GMIylLWFIMHXL6K/l9HmGjTTG3CZkBJo/cU+Hog6h5LmUKJJImzh48YHF2QV3XpJRYr1Y5D5Xh2K7oPXnBzabu4XiUGZSWuRiVTW+W/39QoSCJoeRpagNmVmhw7ObaqGwLYLgu+ydVzXV4yUFghm1IqKYMoAzuvNydk/ecp6jRrtdcPDxDDPq2ZXl+wXw6Y3Fx4cKYz5nuXVOYBbLzxIbtHpZK7WHTEOe4lyHijDBAVIcaTKLY9bKGS2haofSDQAKaa+qbYAByskfKI7twJFiem5PXNR9d3aUhr1yXTLlcLjk9PfVW3CDcuHmTtmk4Pjyi7lo0JW4cHD4y1ldLuzVfedLgjrjE++LVpGyu8upXc8jC/FwxfWKO9Jduk9zb4qS5k0QNIW0qjfmHlD8ZOfuQraiVjN33qW/NWzIiHPDNP4PJVKVZrXjttdc4Pzuja1piiGjf0zQtddNgXUu9uqSua3ZFu8/olRwBAaZbNh217Af8f6qb86qWmZ3JynEFHYJkRM21QHMhio1PGEdjRdOM5AGGyBAoDMmqqUdZGb/y8WT4HcuzCx7cu896ecl6tebB/QdMJhPOH57Rti3z+ZTnX7hFrAKx2h0rd4sSs2n37NXtdwzBt8UVdueikATfLmddbq6LHq567OWDaGm0Fi3IxhaQyMgcOT6lg28qhqk0fqtt5lBwrYA7fi0XDGQ0bcPp6Slvvv46i/MLMKPtWlJKrFYrbt64wf379zjYP2I6mXJ6erIzPu7Wp6QEZExJ4rD9rYB/yYy8YP27pmEzkak5EKm6gfuHVpUN8x0W2Thv8EcOpnLrOrZ2Xg04Wqm9D/DiJoAodLm45N7du1xeXrK+XKGq9Ckxm804OzsjaU9VVTw8fYAZ3L79wm54yI6FoikRY4WqUUUdClslJymduMToGTy51zh3wm8FtAqWnXMctwPlJjzfCOpequQ+JhthFJBYRxpQtMzU3GUUYaiimndqBUhd4uLsjJOTk2GrXUoJU+Xs9CF7VWS1WGJJqSzQiVE317Se4lBF8rp28g51UyMF3d7ylnoIeXOqeh+XI5klUnPszFKG0ZPHySVj91C4qBsom5p+MWMWLPufQXd8jkW/Rt2Vmnx+mhKhMzQlHt6/z2KxJAJd6pnNZrRty8ndt5jPZhwcHLBuamazGX1KXO4wo98tSmwMDWuaQUQzQzQQJv6oMfJK9gGevGfGKzkhLHl16Wz3YlYpLg2OH0FTPwTbmsz3l3SaEWHbCCK3m2qGdCLiVUMVUtDB/1ycnfPWnTs0qxUWhIjRakfTtIgdoCmhCSQY1cEBq8Wa1D/Ju+KejHZceXSTgvh2tRDUUSRTLyIVaD6v4BQCQQ0NMgim9DVc3RnlkZl4G6mEjY8p/kVL5l/msRlDhjOj/1fLUEleTJ2Lv089db3m5PQBdV1zdOOY5vKSrm1BFFUlpcRytWB/b069WoMqz9+6uTM27tynhGColggpDP1Qqps6veWkjlHNvM/9VBsf8Sh5GO1Y1LjR7133hlzNSzoPx3XQWt/pq6qQEid33mK1XKFdT71asX+wz7qpUVXmOXxerZceidVrjg4PSO019Sl91zOZTpCkWG5ISOTSq6ijwXhjiWRkVvPWAkYNFMU5WzmUIyXw+zQnlUMpFwZBbjl2jDjkQeNmB83O3oZ2VOtc0x6envOl117HRAiTQNfXnJ019CPz1PcdJKXve2IMdG3L2fliN0xk15piStd2lP0hIQSCRjQGxKCK/jgxd+zelxVJKRFI2WcIEjbRmefX6ZEtbSoyJCOOZj1Khr8bccCxyEOoI8rJlChhCCwU4xd/4bO8dXKCmiPafesj7+/v03UdKSW07wC8llJVtH2iS9e0wbvvPXb3iMgLWsGMqJ6L9DBAIxHfbhCTEmK1ccaErFHqK7isfFVvpJMRwDjkNIHH7nYA+qREyfmKGn2bSChVdBNa4jMF3nzzDm++dYe2TyAJM3HcTISmrokhEgXavieECYHEtApICKxWq53xcadC6brOzYR4uJsyyKh5T31UHbZ0KznEnVSoSyszaLPdYIA8gnDv7l36vuf5F24N/ur+/RNW6zUvvfQys8kUCdu+Y7lccffeCSn1fOwjHwbg8198lVXb8LGPfJhJiP5eGIN7J/d49dUvcdnUqBhIpAqBKqoXuqoKNePo4JD1ekXfNyQ16romVLpT7GunQkl97/USM6bT3EiQBWI4KCi5advr7JGu733bXOWppY7MysBiEy6WSywK/d0TbhwfM5lMWK9r6r7j9S+/wYfe/wGqKgxdMb0m3nrrLp32JE20nTvirutotefk/gm3n3/BXyiixunpQ+49POX5970PBPoeeu1Q7dw/dgltOxaXK/peSWb0fY+sVkjsrq9Q3rhzj5s3b9DUNTeOj4gx+h7F5PlIzCFwkuR77KMRVdBgjxSJlLRx5J1DNHXdcmkNbZ9o+s61ToV13fDqm28yn0544eYtzJSL5ZLOEik5mrxY1wQJtKlHFe6fLzk8OvboUI2LxZIXX7zNF9/4Ml/68hukDKtgMpixX/3xj7KqL2myRSh/MepWIPC0tFOh/M9XXuGF289zfHSIZkdfGp/39/aoqmoom8YYs0X3lTruuYrlNSEqzhxVvEyeS7HisEcRZNIEqiwuVxwfHpE0cfrwIYSI5BV9vlhQ1zVqNhS86qZlFr3e0vQd/brnwf0HHqAgHMiE1pLvt6kqLhbnTMXo++TjmNFrwrr2Spf/09FOhfLw3j0u7p1w9NxNXgkZsQ1CqCIv3X4/H7z9As/dvIli7O3tueM0HbpNgLzjy+sqSXyjjuaaeMort0uJXpVJjJiZxwV9j5rRqpLUMQFNiZjLyIvViq7rMurgecpyucRme9R1zdnijA++//0cHe1zozogNYlmWTOJQrXn5rZtG2KY0mmizwlqyLvG7NqWg4PQ5ay8z7X5PimkwIOLM2KMnLcNIQT29qYcHx5xNN+HGHMZOAtxwL8yEqDeVF063VNKXosJwQtrRWuqQJ8SXduSshBDyBFUCEwnU4dI+p6UEl3XsRLh4cNTkil70ymSjK7taZuGpmugD9S1kayj6ztuP38bNTdboZSczeiuq/maHh961CWgrRIk5h3CHgS8+fobhC7BtOK542OWL7zAB26/yMHRHiFW1H2DUV6A4LXIVLeskucFs+nU9750necgyWhTjWEeZLTGSTpBU6JLKddyIhqUmIy2qemxIUpcrVb8yhe/wGJxwWRasTfbY7Fesbhc5/JO7xGg+OsRya/dV/M8p8vaYaqoXVPzdfnwnCBCNZ0QJhNfvbm3uF7XVLhpmnTw8PSCe23L/3njLfb7hqgVaVpx8Pwxs8nEm97qNTMi1eEcjUJXt/TrmiTuo156+WUiFaDcf+idJotYMdub0fc906qim06Z7c1YN81QhFuv1ziE6ZXI2WzGtJpwOJnxnZ/4BK0Zi8tL1BKXTUOflLZzTTl/8NAXRgFeM2Ztel01Zb5HrKY0bc3hdMpyueDg6Ii6rrHJFMVQE/roQOWESJ061l3PjckUE+P8Ysl8b8be3pTF4hIIXLZrdD5jgpDqhjaAdh2/8uoXuVg4JvX80SFHh3NuvvC8owdVRUqJX/rc56jbhhdvv0jbNGjd8uD8IQ1KDF4lvXF0E9qen//Pn+GbvuWbubysaeo1fUo0XYMmpdWe1LkvKWObai7aKW+bvX4VtFOhNIslVA6bXHQdcTb13bdVxf7hAR//yIeZTfcIyXjlS6+yWHcES+xVkaZeoVqxf/AcPYnUt0wnFX2bCLFiUlVMJDK56WNO5nu0mpjEij4pXeo5Wy546/Q+bd3Qdi1939PUDQCLS693dG1L1/dMJhPa1DMNkShLUogcVtC2NabK+dlDOhUiHZPpHiQlpX54idx8Pkc1kZIXyPTdQNGvgHYqlNsvv0SIYUikTHzPSVVVLJdLPvu/f5mD+QHTyYRQBZ67cYShhOiJZQiBGIUJFUEC08N9mrZBTFmen5NSoq2bIYQtqxUR9ykZG6tCYJK3JsQYUVXq9ZpON62ufeMgY5MDgna2R3UQ+eg3fpiz80sm9Tk3V2d8uReWs0CqK0yg7Tu6rvOdwwoEHTWw74Z2KpS6qYeXevr+j+AvPBttLO36nq7rc99xjZb+YDO6LuX8o0ekIqWGlCD1iWQ9XiP2KC1U3uWXSgIXAtOcB6U+5RevwcVigZl6T4sqsaqw3qM3ciWz7TqiKfP3fRDtL9mbdcyPp7TVIf3DFXXTYRZ9D2fOp9qmp+96upQ8LB9tOnpa2qlQFovFkESl3ELki+hKPSPD+phu7R+E3DksIKEChKCJRDYPIkTLgGYUZrMZB7M5QYRWe6ztmc8PYAon90+YzmYbjSpdMOY+oOt75vM5fe8dKmttWS7OOd4/5pUvfJ4HFzXrpuNBk1Axd+hBSCnRd537kxAJpqh5XX9XtNN/gOCTn/qkN/JkcLE3oxLf77i1QyprT8wvQQgUQdlwPiEk7R3LyvvaY4zcOHLcK4RA3/fuhDOu9dzxDS4uLrzPd1q5eWrcp0g2Y0Vru87D7BgCqes43j8gijLfm7KuW9bNGkwIseLoxhHHzx0zmUxpmmaoPqaUWNfuu9brNT/z739mJ3HxTjWl7T2ulyBU0VHWcTe6ZpteBFRgcWDYCKS5Kh/EX9q8v7fHfL7HZFJhanRdy2rV0XcdasbebMbB3Gsd9+/fZzqbksSIZqScj5gIkjGw4fnqoCIp0XU9p+fnnr/gr3gX8frPZNoT14E4iRweeii8XK6Gotl8PsfMmF/XXmIzQ817S9q8Kvu+38K0AHfMmUkbDfL+4CrAfG/G0dEx08pzHbVEu26ou46+7RxFDoFpNXEw8eLChXN0yP2TE5Ipoe8dUCRvfcjzkxixjAgIDnROJhV9SsMusaSJKmuW6YS6aZnUNdPplL5PtM2apumoqorJZLKF6e2CdltPaTv3B5nRwwvQsrlKSYlBSBgVgWDKtApUswk35keEWIHkBuvUsawb+q6j7XvvFhGY7885nB9A27PuGtq+Y76/T6eJi/sPqSb+du/Ly0vywzcMy/tktiASoE+J6XTKwf4+i+Uya5Nvmt2bV0yqCTFW1Hk+MQSmk4iasrg4p6omV/ZgPh3tvPJYhFJMRZSAio3MVmQvwtHBIZPKV7uEQJ961Dw5S0lJfU/bdQ6hqHJwcMDx0THrywu6dc3FconEilu3bqGqXJyfIzFQTSacn53RpzQsjiHZG/cn2+gfJ1OjqRviQeTg4IDV8nJoZUo9xGiDc08puS8zL0+ruUlL2UftgnYLs6x8dZawOMZINZ0yqyqO9w+ZTyLT2ZRYTemSh6nLeg1m9Kkn9Uafwb4i1KOjI3fGbcv6YsH5xTlN37J/eMz+/j7L5ZKmrqkmE2azGfdPHwx1jiKE2Ww21Dskm07Y7J0vAONi4c0PYpAseSuTGk3bMak6352WA5fhnZMS8z8psruA6dk//3QNaedbIZ7R09MzoVxDeiaUa0jPhHIN6ZlQriE9E8o1pP8LD/VFJVTKXKUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_model(model_ft)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocess_transform = transforms.Compose([\n",
    "        transforms.Resize(256),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 1.9017, -0.5616, -0.9316]], device='cuda:0', grad_fn=<AddmmBackward>)\n",
      "[('city', 87.414306640625), ('highway', 7.443892955780029), ('ramp', 5.141805648803711)]\n"
     ]
    }
   ],
   "source": [
    "from PIL import Image\n",
    "# https://www.cnblogs.com/ttweixiao-IT-program/p/11977884.html\n",
    "image_PIL = Image.open('10.jpg')\n",
    "#\n",
    "image_tensor = preprocess_transform(image_PIL)\n",
    "# 以下语句等效于 image_tensor = torch.unsqueeze(image_tensor, 0)\n",
    "image_tensor.unsqueeze_(0)\n",
    "# 没有这句话会报错\n",
    "image_tensor = image_tensor.to(device)\n",
    " \n",
    "out = model_ft(image_tensor)\n",
    "print(out)\n",
    "# 得到预测结果，并且从大到小排序\n",
    "_, indices = torch.sort(out, descending=True)\n",
    "# 返回每个预测值的百分数\n",
    "percentage = torch.nn.functional.softmax(out, dim=1)[0] * 100\n",
    "print([(class_names[idx], percentage[idx].item()) for idx in indices[0][:5]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ConvNet as fixed feature extractor\n",
    "----------------------------------\n",
    "\n",
    "Here, we need to freeze all the network except the final layer. We need\n",
    "to set ``requires_grad == False`` to freeze the parameters so that the\n",
    "gradients are not computed in ``backward()``.\n",
    "\n",
    "You can read more about this in the documentation\n",
    "`here <https://pytorch.org/docs/notes/autograd.html#excluding-subgraphs-from-backward>`__.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_conv = torchvision.models.resnet18(pretrained=True)\n",
    "for param in model_conv.parameters():\n",
    "    param.requires_grad = False\n",
    "\n",
    "# Parameters of newly constructed modules have requires_grad=True by default\n",
    "num_ftrs = model_conv.fc.in_features\n",
    "model_conv.fc = nn.Linear(num_ftrs, 3)\n",
    "\n",
    "model_conv = model_conv.to(device)\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# Observe that only parameters of final layer are being optimized as\n",
    "# opposed to before.\n",
    "optimizer_conv = optim.SGD(model_conv.fc.parameters(), lr=0.001, momentum=0.9)\n",
    "\n",
    "# Decay LR by a factor of 0.1 every 7 epochs\n",
    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train and evaluate\n",
    "^^^^^^^^^^^^^^^^^^\n",
    "\n",
    "On CPU this will take about half the time compared to previous scenario.\n",
    "This is expected as gradients don't need to be computed for most of the\n",
    "network. However, forward does need to be computed.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/24\n",
      "----------\n",
      "train Loss: 0.8547 Acc: 0.6118\n",
      "val Loss: 0.3368 Acc: 0.9600\n",
      "\n",
      "Epoch 1/24\n",
      "----------\n",
      "train Loss: 0.5912 Acc: 0.7824\n",
      "val Loss: 0.1668 Acc: 0.9564\n",
      "\n",
      "Epoch 2/24\n",
      "----------\n",
      "train Loss: 0.3745 Acc: 0.8471\n",
      "val Loss: 0.1074 Acc: 0.9673\n",
      "\n",
      "Epoch 3/24\n",
      "----------\n",
      "train Loss: 0.2497 Acc: 0.9000\n",
      "val Loss: 0.1379 Acc: 0.9600\n",
      "\n",
      "Epoch 4/24\n",
      "----------\n",
      "train Loss: 0.2847 Acc: 0.8941\n",
      "val Loss: 0.1888 Acc: 0.9455\n",
      "\n",
      "Epoch 5/24\n",
      "----------\n",
      "train Loss: 0.3138 Acc: 0.8706\n",
      "val Loss: 0.1179 Acc: 0.9636\n",
      "\n",
      "Epoch 6/24\n",
      "----------\n",
      "train Loss: 0.3980 Acc: 0.8235\n",
      "val Loss: 0.1811 Acc: 0.9527\n",
      "\n",
      "Epoch 7/24\n",
      "----------\n",
      "train Loss: 0.2749 Acc: 0.8882\n",
      "val Loss: 0.1156 Acc: 0.9636\n",
      "\n",
      "Epoch 8/24\n",
      "----------\n",
      "train Loss: 0.2209 Acc: 0.9353\n",
      "val Loss: 0.0936 Acc: 0.9673\n",
      "\n",
      "Epoch 9/24\n",
      "----------\n",
      "train Loss: 0.2568 Acc: 0.9353\n",
      "val Loss: 0.1169 Acc: 0.9673\n",
      "\n",
      "Epoch 10/24\n",
      "----------\n",
      "train Loss: 0.2524 Acc: 0.9235\n",
      "val Loss: 0.0999 Acc: 0.9673\n",
      "\n",
      "Epoch 11/24\n",
      "----------\n",
      "train Loss: 0.1865 Acc: 0.9529\n",
      "val Loss: 0.1249 Acc: 0.9636\n",
      "\n",
      "Epoch 12/24\n",
      "----------\n",
      "train Loss: 0.1895 Acc: 0.9235\n",
      "val Loss: 0.0946 Acc: 0.9709\n",
      "\n",
      "Epoch 13/24\n",
      "----------\n",
      "train Loss: 0.2935 Acc: 0.8941\n",
      "val Loss: 0.1129 Acc: 0.9673\n",
      "\n",
      "Epoch 14/24\n",
      "----------\n",
      "train Loss: 0.2324 Acc: 0.9118\n",
      "val Loss: 0.1301 Acc: 0.9636\n",
      "\n",
      "Epoch 15/24\n",
      "----------\n",
      "train Loss: 0.2572 Acc: 0.8941\n",
      "val Loss: 0.0936 Acc: 0.9673\n",
      "\n",
      "Epoch 16/24\n",
      "----------\n",
      "train Loss: 0.3124 Acc: 0.9000\n",
      "val Loss: 0.1205 Acc: 0.9673\n",
      "\n",
      "Epoch 17/24\n",
      "----------\n",
      "train Loss: 0.2154 Acc: 0.9118\n",
      "val Loss: 0.1010 Acc: 0.9673\n",
      "\n",
      "Epoch 18/24\n",
      "----------\n",
      "train Loss: 0.2171 Acc: 0.9235\n",
      "val Loss: 0.1015 Acc: 0.9673\n",
      "\n",
      "Epoch 19/24\n",
      "----------\n",
      "train Loss: 0.2194 Acc: 0.9235\n",
      "val Loss: 0.1023 Acc: 0.9673\n",
      "\n",
      "Epoch 20/24\n",
      "----------\n",
      "train Loss: 0.3540 Acc: 0.8824\n",
      "val Loss: 0.1188 Acc: 0.9636\n",
      "\n",
      "Epoch 21/24\n",
      "----------\n",
      "train Loss: 0.1868 Acc: 0.9471\n",
      "val Loss: 0.1360 Acc: 0.9636\n",
      "\n",
      "Epoch 22/24\n",
      "----------\n",
      "train Loss: 0.2794 Acc: 0.8941\n",
      "val Loss: 0.1233 Acc: 0.9636\n",
      "\n",
      "Epoch 23/24\n",
      "----------\n",
      "train Loss: 0.2439 Acc: 0.9000\n",
      "val Loss: 0.1272 Acc: 0.9636\n",
      "\n",
      "Epoch 24/24\n",
      "----------\n",
      "train Loss: 0.2331 Acc: 0.9118\n",
      "val Loss: 0.1337 Acc: 0.9600\n",
      "\n",
      "Training complete in 10m 49s\n",
      "Best val Acc: 0.970909\n"
     ]
    }
   ],
   "source": [
    "model_conv = train_model(model_conv, criterion, optimizer_conv,\n",
    "                         exp_lr_scheduler, num_epochs=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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nkvKM/bVN07K6WhBQKl+xXq148M59nn3uudylAwcH+ywWCyZVRQyBzWr5/vn1GNotKL13NKRbxmQjNfU4hvYsHS1H6O2IjtVOzhA76SHcNrXKkCIBIaDRUuY5n9uDMxo1hsjy/JLQtiw3a6qqYrNcsb5acri3TzWdIAgxRGbTGSGumE3nSRvsiHbczZKN+ajdxvcsDHhXQXl4jTlJ6RB1OJcfxcDlaiAG4vx2G5AVFrrBtXbj6P46uazSNF/fQzqS2VIGhqiRLgY6jWgXiGpcXVwSQ2S1XCHeMZ1OU1+Bc6CwWq0e74B8FbTzXmIgaSK1FHeQlhRYVJQO76qk13MvrvOGETHN6X7RvuXH5YxhYVuZ4Un16WCitJwxSOCoMrIlQXZNnh5Jo3SB9WJJs9lQVRVf+fJbOOeYTCd0XUe76ZjP58xmU5wTptMJl5eXzOfz3fFxZyNBalzL3tT1tR2QZvfwHlIBSZNjgGa9XpJlOdCkxCeyle3Fiks7/HmT1KShRl8cVkvOR/+MOU2TDXz5K0/b1HWKTQwe3H+HGCMhBowkRU4c6/UaEWM2m6DWMZl6VG9pQpKRPdEYceYQ7wZpMUV1kCiNmhST5EBRU5ZYs3+kcTDw44Y5LNXat1OKqSbSRzWx1PZl8MKynTIl94kN9RKR1HC+Wq14+OAhm9WK0LasF0v2Dg9wIqxXa4pFMjU26zUaIhM/QXY4vXce0d/UQdBLTY4DksG20SWWATJUtTSYAMlDLfk0bGCsxohqvsa0Z+516VHVESgUXziNO3rWqJGu62jWG0yV6WzGcrUiaLoPwGw2w2NUGBojlxeXtOsGicbB/i3NEjvN6scJXqohm5s74wsQZa1Hql2M7IRB30NcAHKCuyHPVRaBujjIi5HSNpbtw7glaftiy43kuWXIIBKQrmPT1CwWC5qmwVcVVYj9zJ1UnmWtNF1HjJGjO3e5PD5lsbi8vTV6jZqjb8GqiKrLBaWh/mjOUh5KB8+sN+WaaiIuGuRla7LVOip9MDhKJ24lKmOMg0dl1qsxREeSkUq7RgLF5zRLbLsEUIzEtmNvPqdZbwgh0I6a7bz3zObTlH0g+diTyS0NHtUUZ4IIaIg4Z0QTvBpW1qX0xbyYmVxKxobzFcRkMLUzxPmUvtIctY+Dd0eSKrPU1CAgMUlAHLmnnnROESjDSkKh70oJRMyMFiV2HaFtaZoGjZHZdEpX10wmE6qqwnvPOw8e0CxX3Lt3j6gRcY7p/mxnfNxxRG/9Yk4RUFWm+CQdcdyaUBoQUtreMNDkMguCZuNsseu7YVw29m4rqs+Kz1nvtBXvqoiS5RTNeDm9ja7v818xslwsaOtNWhlukbpJ0lK3Dc/fOeKlFz9CiEq9qZlWE+r1hunelMvTBfP9vZ2xcbegRE2z3jQlE51gMVmI6AQf6VVQ7Juos6oRt5XbKgFjyW1pntaxs6FmM2rMhkG1pXVvI7uSn62QjRyNcr+mbVheXLFcLrm4vGQ2mbJYXvQbK1jsWFxdcn5+zt07BzjnODs9w1eej+wwGQk7BsU0EEQRk9ROakL0AjEi6ggTeq+qdL60XZeXS1guATO4riUZxjUPTJMNKMCVKL2vetrQUb/1fBgOTcOrpq6VXKJeLpasN2tEhFk1YTqb0jYNi9zZHQ73MTPOLy6oqor5fE4XOkJQ6tWSF5/bHTA7BSWEgNdSg1ecOarMWEGQOCyvSy+pfhJiZOIrNOYauKN3Q13uKbYcZ6SZH3NACU0MfaeMdz7FHp3SxI5qMkn3H6XnU5+wErM61Rip65rQNVTiWF5ccvfoCDNj6iuOjo5omg2nJ6dsVmtCGzk9O6Nyjlc/9jGOH57SbTpme7fUpmiImLeU9pC0OYCqplXCkgJJyymRSoRoyuX5FQ9PTvnUJz6e1VwAhMuLC04vr/jQhz7EnaNDxAKpvWVQS9GMt7/yNnXTUM2m/JZXXsUwLq4uuP/gmIPDI1598SOPPKflOAOE+/ff4fTklJde+jCYMptNOD05xVcVR4eH3D3Yp/Ge480JzzzzDOcXXwbn+NAL92jqOinYEKgmu0uz7BSULgaqsi+DSxFy2X1CEYiCc1WKnmNqvD45PWFd16zqmsuLC+7eOWIyqbj/4D4WhBMzFosrnn/2HkLXj22aAs7lcpnqIk6IXQCUpmlZ1xsuF0vu3bnDbLr9M0tQ2baB1157DcWoKuHOnTt9vOGdo6oqQgg475hMZlxeXnJ0dMDh0SGHh4eICF1nNG1k23f/YLRTUGIIWFm+nANBT58Xxpvr1wmmvrnkUZkZ/+fNNwnZvmBGGxXEsb66pKo8R3v7+CpvIRJSubbtAkGVKEZlRhs6nKQ4oyQFTs/PeOHes1xP7EdTTo6PWa1WTA/2MDOurhaYGZeLK545uguk9MtqtaKqHJNqAsBsPmdvb4+2bXFiYJHj4+Od8XGnoDRNh688EnXY8CZav+lBkg7rc1dOUkrdzFgul3jvaUJkcXXVL5uKMSIivH7/bcSMV156kYl3nF+tWNdrWo2IGZ3rWC2XiPeELiZVasaD01MO9udbvb5mxuXlJf/ji1+kDh1Se9brNYeHd5jP99KSjGnFC88+x4MH76QakCmdBjZtQ7VcEe7eZV1vuFovqNuG9eaW1uibrkWCMKkmfc+wrypijFsLigAqM1SEqEZQTTkqAc25phRGGIvVBg0RP6mYT6YsF0sODvY5uzin6zqqvPxaVYlmHD94h6qqEHGodqjljni/rV7u339AEwIxRjZNTV3XgKOu65T8FNjUNZvQMfGe6dQjznG5OGUy22O1aVhtGoLmVV23dX3K22enHMxn7M/2mFUVkr0o5xyd5deuw4lDcTgxwqYhaEdQwwvU9QaziFjgbLHmv/7K51NNRSQ1jDvHwXzGt3/btyZjXVV4EUIIbJqay9Waw6NDupyw7NqO86sFd/YPUlSfk5MPT05ouwZVI8ZI0waaJuWwQlCOzy6I8bS3PwDz+Zy6aXAiPHj4ECdC03S0XUPUW1p5PF+sWNUbvFzw8vMfpnIer6kP7I3XX8d5T7eJXD44TtF5ZXiD2d07+L19DuYV3WEkhoiacXG1IGpEQ0hl+sqj3nO13hBiUluQpMsseWN7e3vJluVtocwM80LbdVkSk1Ru2oYuaj9G3bY0XUtsO4Ip2nUp9dK2faa5LIwNpjiEECPrekMXwi1eyRVjikm8cLVcgRrT2YTKeS5XC4J56tUaXylTc2gIVNMZXTTOz85YiXF8eopERRycLZfszyeYTjATurZNmVvvaEOHR6icIL5C1dhsaur1up/ZbdchIjSbBvUJaIuBLnQ0bUdoFRNFTDk/OyOacufuXWgbDEuqDCOGgBPH1dVV7/F99OUXaduW2WxK0zXUq1vbtmq88PxzyX54D6qcXVzQdYFN3dG0KzzQ1U3uXIQ2toiv8AYSBZk51CLtepNc6pyKqZuOtm3Zm05QU7quw09nqdjlPSG2XF5eUlUVVZVCxjYEoinrTd13UWrX5gliKEkqvRnLrsMwZvM5q9UqSbUG9g8PCYtlWh1cedqmods0VN4zOzwEU+b7c+Z3jnbGxZ2C8vkv/W9++Uuvgbi0laAI02pC9PDKhz/MfL7P6uIca1uiGpPZDOcVCzWq4PHQRrwJe9MZp+sVXoTVas1ynWbiDJh4z8XiigrHs3ePcNU0MdI5jo4Sc1SVg4MD1JT1ZsPx8TFN3aQssCoxqzGcIVWFbZqURjk/73eUkGisl0tCl1L6begwYN3WvPHmm727HFV5e/OVnfFxp6C0KKYO7yKaN9bstAMT2rrBzysiScd/4ps/xXxvj+lsymQySUGlwXq9Yj6dU02E0899jt/49TdAhMPDIy4WV8SzM1796Mt85PkXQI35dErTdUynU9Q0G+w1J6dndKFLTIuRNnRs1hvuHt1hWa9TuVqE+WxOrIy6qXMjYFoVVlpgUzXI+r4kw2ibmpOTGsz63SjCbV3z+C2f/CRN09C1XR+nVJXPnR/GbLrH3aM7VJK8sBACvk4uc9O0RI2YZuNqyhuvvw4ihNBxdn6GOcGCsd4s+dUv/BpmSggBVaNr27R8WoTKV/1GPaZG3TYEjaDGOycP8ZOq7165XC7xIsxmM2KM7E3nRFW6LhBLHcdi7rjJWWUl7T8pQlQFM6bT6c74uFNQHj68nzyf0T6RThzLpeV+Yo8TN6TUZdghzyypnM2mSS6mxZzenxBiBx68GJV3HJ+dMqsmqbBFWpg09dWw32OMVNWcaJHLxdUo06yoGbFpwazPHjQh0GRva7VeU3nP/v4BkyovscMRgqCW0jgpORpzSRpAbm85eLlc9tXAorND16XZlKlsvAEMm0BDz7i00quiqqYcHd6h7Wq8n/RV3QJ6AaGMHTWmGVtN8RPP+fkZnenWBOhLAvn5Qs4WFG+tvIYQqJsmTSpgNpmyv79P9IldMQTa2BJCSDUciaPG8w9OOwXl5PQUGDXlkZtHM/Odc1TjfSUzkwpjTTWlNGJadVtI8raAzjlm83mK3vtlvdJ3wOzP5mgIXKwW1KFLkmgGectdtf4fmek9u/Jqo06ZqEM93wFdCKzrDc5Xff/z/rxiOpmBCm0X6G7rzniQAPDeM52k5F2JiLuQ1gl2MbXylHNhAA6AAEZIM9RVzGaz1NQQIt5X/bVlXOccsyptDnpynmINl2d4Sde4Udqmr8lo7pzMduH555/n7OyMtm3zWsd0flnZFfI4ku+9Xq/7JRt7+3scHJV//uuD0269r7ZFENqmpfbb26ePpae8H0fBRlJdM+eYzmYIRsjdicl4CNGSioox9mMc7B+goePs4oJQuiZLCbdIBdv9YP3+kAUk4PT4mLt373J+ft5LVN9L1u/3lVptezWnKZLvrrqkundEu62n5AhaRNKiUUlNEJ7S/5VmcHQOMY93wmxS5YbpkJOKgTYy6P9giKQCQNS8j5gZ84lnr5rx8OF9gqv6hrwxGED/eYvJ5f1IEsyM4+PjLcDGtfwCxHiM8Viqt9SmhBAe+dcfAMx7oineHCbCtBIO5ntgkbYNqTfXu37Dgxiyfi7Xx5aoSuUdMcAzRwe09YbfPL9MOwuFrmfu0GSe6/VFKnuHQLcZP2K+IGz33NDnvcYgFFu0Jem3Nfe12Wx6MNIeX6nq6J3jmdkelXhWsaOJRrdeUzlhWqUUS7TkDQG5DpO65UpjgwdmE8/ce05OjgmkjT8lhNRBnBld9qYvzH5k0+hrDRVFwh6RovwczrkhsTnujmFbJe5SUp7+80+3kHbf4P2UPjA9BeUW0lNQbiE9BeUW0lNQbiE9BeUW0v8F39w90vauXBkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_model(model_conv)\n",
    "\n",
    "plt.ioff()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Further Learning\n",
    "-----------------\n",
    "\n",
    "If you would like to learn more about the applications of transfer learning,\n",
    "checkout our `Quantized Transfer Learning for Computer Vision Tutorial <https://pytorch.org/tutorials/intermediate/quantized_transfer_learning.html>`_.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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